2 research outputs found
Regulation of Bacterial Gene Expression by Protease-Alleviated Spatial Sequestration (PASS)
In
natural microbial systems, conditional spatial sequestration of transcription
factors enables cells to respond rapidly to changes in their environment
or intracellular state by releasing presynthesized regulatory proteins.
Although such a mechanism may be useful for engineering synthetic
biology technologies ranging from cell-based biosensors to biosynthetic
platforms, to date it remains unknown how or whether such conditional
spatial sequestration may be engineered. In particular, based upon
seemingly contradictory reports in the literature, it is not clear
whether subcellular spatial localization of a transcription factor
within the cytoplasm is sufficient to preclude regulation of cognate
promoters on plasmid-borne or chromosomal loci. Here, we describe
a modular, orthogonal platform for investigating and implementing
this mechanism using protease-alleviated spatial sequestration (PASS).
In this system, expression of an exogenous protease mediates the proteolytic
release of engineered transcriptional regulators from the inner face
of the <i>Escherichia coli</i> cytoplasmic membrane. We demonstrate that PASS mediates robust,
conditional regulation of either transcriptional repression, <i>via</i> tetR, or transcriptional activation, by the λ
phage CI protein. This work provides new insights into a biologically
important facet of microbial gene expression and establishes a new
strategy for engineering conditional transcriptional regulation for
the microbial synthetic biology toolbox
Predicting the Dynamics and Heterogeneity of Genomic DNA Content within Bacterial Populations across Variable Growth Regimes
For
many applications in microbial synthetic biology, optimizing
a desired function requires careful tuning of the degree to which
various genes are expressed. One challenge for predicting such effects
or interpreting typical characterization experiments is that in bacteria
such as <i>E. coli</i>, genome copy number varies widely
across different phases and rates of growth, which also impacts how
and when genes are expressed from different loci. While such phenomena
are relatively well-understood at a mechanistic level, our quantitative
understanding of such processes is essentially limited to ideal exponential
growth. In contrast, common experimental phenomena such as growth
on heterogeneous media, metabolic adaptation, and oxygen restriction
all cause substantial deviations from ideal exponential growth, particularly
as cultures approach the higher densities at which industrial biomanufacturing
and even routine screening experiments are conducted. To meet the
need for predicting and explaining how gene dosage impacts cellular
functions outside of exponential growth, we here report a novel modeling
strategy that leverages agent-based simulation and high performance
computing to robustly predict the dynamics and heterogeneity of genomic
DNA content within bacterial populations across variable growth regimes.
We show that by feeding routine experimental data, such as optical
density time series, into our heterogeneous multiphasic growth simulator,
we can predict genomic DNA distributions over a range of nonexponential
growth conditions. This modeling strategy provides an important advance
in the ability of synthetic biologists to evaluate the role of genomic
DNA content and heterogeneity in affecting the performance of existing
or engineered microbial functions